Coding With David

  In modern development, we rely heavily on AI to assist with coding, debugging, and architectural decisions. But every once in a while, a problem comes along that AI can’t quite crack—at least not without a developer who understands the underlying language and can think beyond AI’s suggestions. Recently, I ran into exactly that scenario while working with an application that uses Excel spreadsheets as a data source. Yes, it’s not ideal. No, that part isn’t changing. The real issue was something far more painful: The spreadsheet columns kept changing positions. Every time a new spreadsheet arrived, the columns shifted around unpredictably. This caused the parser to misalign fields, break imports, and generally ruin my day. What We Tried (with AI’s Help) I paired with AI to generate a variety of solutions: Custom Excel parser using NuGet packages CSV parser variants Multiple fallback strategies Different libraries for column mapping and validation These solution...

  Hands-on impressions using a Snapdragon X Elite laptop Local LLMs have exploded in popularity, and with new hardware like the Snapdragon X Elite , it’s finally practical to run powerful AI models entirely on your machine—fast, private, offline, and inexpensive. In this guide, I’ll walk you through three of the most popular tools for running local models: Ollama LM Studio Microsoft Foundry For each, I’ll cover: How to install it Minimum hardware requirements Rough model availability Pros and cons My personal performance testing results All tests were done on: 🧑‍💻 My Test Hardware Microsoft Surface Laptop, 7th Edition Component Details CPU Snapdragon X Elite (X1E80100), 12 cores @ 3.40 GHz RAM 32 GB OS Windows 11 Home, Build 26200 GPU / AI Acceleration DirectX 12 / NPU support Notes ARM-based architecture This hardware is extremely efficient for local inference—especially for optimized models. 1. Ollama ✔️ “The easiest way to run local LLM...

  Micro Learning with AI In a world where technology evolves faster than we can schedule meetings, traditional long-form learning is becoming harder to maintain. Developers, designers, analysts, and everyday professionals are turning to micro-learning —a structured approach to learning that breaks topics into small, digestible units. Micro-learning isn't just a trend; it's a productivity multiplier. Whether you're learning C#, React, Python, AI agents, or something outside tech, micro-learning can help you absorb more information in less time, with better long-term retention. In this article, we’ll explore what micro-learning is, how it works, its benefits, its downsides, and walk through a real example you can use today. What Is Micro-Learning? Micro-learning is the process of breaking a subject into small, highly focused lessons—usually completed in 5–15 minutes. Instead of binge-learning for hours, you learn a small concept, apply it immediately, and move ...

How to use AI to Maintain Coding Standards Blazor is one of the fastest-growing .NET frameworks for building rich, interactive web applications. But when multiple developers work on a Blazor project—especially with varying skill levels—maintaining consistent coding standards becomes a real challenge. Razor files balloon to thousands of lines. Component naming conventions drift. UI structure becomes inconsistent. Architecture slowly erodes. This is where AI becomes a force multiplier. AI doesn’t replace senior developers. Instead, it automates the repetitive checking, teaching, reviewing, and refactoring tasks that drain development teams and erode quality. With the right prompts, templates, and workflows, AI can enforce architecture rules, naming conventions, component patterns, and test quality—without forcing a single "code cop" to police everyone. This article teaches you how to use AI to maintain coding standards in multi-developer Blazor projects using: AI-powered ...